Advertisement

Broker-Insights: An Interactive and Visual Recommendation System for Insurance Brokerage

  • Paul Dany AtauchiEmail author
  • Luciana Nedel
  • Renata Galante
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11542)

Abstract

The black box nature of the recommendation systems limits the understanding and acceptance of the recommendation received by the user. In contrast, user interaction and information visualization play a key role in addressing these drawbacks. In the brokerage domain, insurance brokers offer, negotiate and sell insurance products for their customers. Support brokers into the recommendation process can improve the loyalty, profit, and marketing campaign in their client portfolio. This work presents Broker-Insights, an interactive and visualisation-based insurance products recommender system to support brokers into the decision-making (recommendation) at two levels: recommendations for a specific potential customer; and recommendations for a group of customers. Looking for offering personalized recommendations, Broker-Insights provides a tool to manage customers information in the recommendation task and a module to perform customers segmentation based on specific characteristics. With the help of an eye-tracker, we evaluated Broker-Insigths usability with ten naive users on the offline fashion and also performed an evaluation in the wild with three insurance brokers. Results achieved show that data mining methods, while combined with interactive data visualization improved the user experience and decision-making process into the recommendation task, and increased the products recommendation acceptance.

Keywords

Recommender system Visual analytics Data mining Insurance brokerage 

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 6, 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22, 207–216 (1993)CrossRefGoogle Scholar
  3. 3.
    Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)Google Scholar
  4. 4.
    Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)CrossRefGoogle Scholar
  5. 5.
    Valdez, A.C., Ziefle, M., Verbert, K.: HCI for recommender systems: the past, the present and the future. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 123–126. ACM (2016)Google Scholar
  6. 6.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 5, 603–619 (2002)CrossRefGoogle Scholar
  7. 7.
    Gupta, A., Jain, A.: Life insurance recommender system based on association rule mining and dual clustering method for solving cold-start problem. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(7), 1356–1360 (2013)Google Scholar
  8. 8.
    Hahsler, M., Chelluboina, S.: Visualizing association rules: introduction to the r-extension package arulesViz. R Project Module, pp. 223–238 (2011)Google Scholar
  9. 9.
    Hahsler, M., Karpienko, R.: Visualizing association rules in hierarchical groups. J. Bus. Econ. 87(3), 317–335 (2017)CrossRefGoogle Scholar
  10. 10.
    He, C., Parra, D., Verbert, K.: Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Syst. Appl. 56, 9–27 (2016)CrossRefGoogle Scholar
  11. 11.
    Jugovac, M., Jannach, D.: Interacting with recommenders-overview and research directions. ACM Trans. Interact. Intell. Syst. (TiiS) 7(3), 10 (2017)Google Scholar
  12. 12.
    Karimi, M., Jannach, D., Jugovac, M.: News recommender systems-survey and roads ahead. Inf. Process. Manag. 54(6), 1203–1227 (2018)CrossRefGoogle Scholar
  13. 13.
    Lewis, C., Rieman, J.: Task-centered user interface design. A practical introduction (1993)Google Scholar
  14. 14.
    Lewis, J.R.: The system usability scale: past, present, and future. Int. J. Hum.-Comput. Interact. 34(7), 577–590 (2018). Taylor & FrancisCrossRefGoogle Scholar
  15. 15.
    Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert Syst. Appl. 41(4), 2065–2073 (2014)CrossRefGoogle Scholar
  16. 16.
    Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015)CrossRefGoogle Scholar
  17. 17.
    Mitra, B.P.S., Chaudhari, N., Patwardhan, B.: Leveraging hybrid recommendation system in insurance domain. Int. J. Eng. Comput. Sci. 3(10), 8988–8992 (2014)Google Scholar
  18. 18.
    Mukherji, A., et al.: FIRE: a two-level interactive visualization for deep exploration of association rules. Int. J. Data Sci. Anal. 7(3), 201–226 (2019)CrossRefGoogle Scholar
  19. 19.
    Pandey, A.K., Rajpoot, D.S.: Resolving cold start problem in recommendation system using demographic approach. In: 2016 International Conference on Signal Processing and Communication (ICSC), pp. 213–218. IEEE (2016)Google Scholar
  20. 20.
    Qazi, M., Fung, G.M., Meissner, K.J., Fontes, E.R.: An insurance recommendation system using Bayesian networks. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 274–278. ACM (2017)Google Scholar
  21. 21.
    Rahman, S.S.A., Norman, A.A., Soon, K.: MyINS: a CBR e-commerce application for insurance policies. Electron. Commer. Res. 5(1), 373–380 (2006)Google Scholar
  22. 22.
    Rokach, L., Shani, G., Shapira, B., Chapnik, E., Siboni, G.: Recommending insurance riders. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 253–260. ACM (2013)Google Scholar
  23. 23.
    Sobhanam, H., Mariappan, A.: Addressing cold start problem in recommender systems using association rules and clustering technique. In: 2013 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–5. IEEE (2013)Google Scholar
  24. 24.
    Solanki, S.K., Patel, J.T.: A survey on association rule mining. In: 2015 Fifth International Conference on Advanced Computing & Communication Technologies (ACCT), pp. 212–216. IEEE (2015)Google Scholar
  25. 25.
    Xu, W., Wang, J., Zhao, Z., Sun, C., Ma, J.: A novel intelligence recommendation model for insurance products with consumer segmentation. J. Syst. Sci. Inf. 2(1), 16–28 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Informatics – Federal University of Rio Grande do Sul (UFRGS)Porto AlegreBrazil

Personalised recommendations